Cargando…

Federated learning and differential privacy for medical image analysis

The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly st...

Descripción completa

Detalles Bibliográficos
Autores principales:	Adnan, Mohammed, Kalra, Shivam, Cresswell, Jesse C., Taylor, Graham W., Tizhoosh, Hamid R.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Nature Publishing Group UK 2022
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8816913/ https://www.ncbi.nlm.nih.gov/pubmed/35121774 http://dx.doi.org/10.1038/s41598-022-05539-7

Ejemplares similares

Decentralized federated learning through proxy model sharing
por: Kalra, Shivam, et al.
Publicado: (2023)

Medical imaging deep learning with differential privacy
por: Ziller, Alexander, et al.
Publicado: (2021)

PLDP-FL: Federated Learning with Personalized Local Differential Privacy
por: Shen, Xiaoying, et al.
Publicado: (2023)

Privacy-first health research with federated learning
por: Sadilek, Adam, et al.
Publicado: (2021)

A federated learning differential privacy algorithm for non-Gaussian heterogeneous data
por: Yang, Xinyu, et al.
Publicado: (2023)

In the Pursuit of Privacy: The Promises and Predicaments of Federated Learning in Healthcare
por: Topaloglu, Mustafa Y., et al.
Publicado: (2021)

Federated Learning for Privacy-Aware Human Mobility Modeling
por: Ezequiel, Castro Elizondo Jose, et al.
Publicado: (2022)

Differential privacy preserved federated transfer learning for multi-institutional (68)Ga-PET image artefact detection and disentanglement
por: Shiri, Isaac, et al.
Publicado: (2023)

Flimma: a federated and privacy-aware tool for differential gene expression analysis
por: Zolotareva, Olga, et al.
Publicado: (2021)

A Critical Evaluation of Privacy and Security Threats in Federated Learning
por: Asad, Muhammad, et al.
Publicado: (2020)

Federated learning for preserving data privacy in collaborative healthcare research
por: Loftus, Tyler J, et al.
Publicado: (2022)

Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning
por: Peng, Kaixin, et al.
Publicado: (2023)

A Differential Privacy Strategy Based on Local Features of Non-Gaussian Noise in Federated Learning
por: Wang, Xinyi, et al.
Publicado: (2022)

Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis
por: Lee, Junghye, et al.
Publicado: (2018)

Exploring the Relationship Between Privacy and Utility in Mobile Health: Algorithm Development and Validation via Simulations of Federated Learning, Differential Privacy, and External Attacks
por: Shen, Alexander, et al.
Publicado: (2023)

Privacy-preserving GWAS analysis on federated genomic datasets
por: Constable, Scott D, et al.
Publicado: (2015)

Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning
por: Kurniawan, Hendra, et al.
Publicado: (2022)

Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving
por: Luan, Zhirong, et al.
Publicado: (2023)

A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems
por: Gu, Xin, et al.
Publicado: (2023)

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review
por: Brauneck, Alissa, et al.
Publicado: (2023)

Application of reinforcement learning for segmentation of transrectal ultrasound images
por: Sahba, Farhang, et al.
Publicado: (2008)

Medical Imaging Applications of Federated Learning
por: Sandhu, Sukhveer Singh, et al.
Publicado: (2023)

Federated learning for medical imaging radiology
por: Rehman, Muhammad Habib ur, et al.
Publicado: (2023)

Splitting chemical structure data sets for federated privacy-preserving machine learning
por: Simm, Jaak, et al.
Publicado: (2021)

Privacy-preserving federated neural network learning for disease-associated cell classification
por: Sav, Sinem, et al.
Publicado: (2022)

Analysis of Privacy-Enhancing Technologies in Open-Source Federated Learning Frameworks for Driver Activity Recognition
por: Novikova, Evgenia, et al.
Publicado: (2022)

Approaches to Federated Computing for the Protection of Patient Privacy and Security Using Medical Applications
por: Ahmed, Osman Sirajeldeen, et al.
Publicado: (2022)

Quantum machine learning with differential privacy
por: Watkins, William M., et al.
Publicado: (2023)

Analysis of Application Examples of Differential Privacy in Deep Learning
por: Shen, Zhidong, et al.
Publicado: (2021)

Distributed Detection of Malicious Android Apps While Preserving Privacy Using Federated Learning
por: Lee, Suchul
Publicado: (2023)

Privacy-by-Design Environments for Large-Scale Health Research and Federated Learning from Data
por: Zhang, Peng, et al.
Publicado: (2022)

A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine
por: Hiwale, Madhuri, et al.
Publicado: (2023)

FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing
por: Cheng, Zhimo, et al.
Publicado: (2023)

Federated Learning for Medical Image Analysis with Deep Neural Networks
por: Nazir, Sajid, et al.
Publicado: (2023)

COLLAGENE enables privacy-aware federated and collaborative genomic data analysis
por: Li, Wentao, et al.
Publicado: (2023)

Balancing data privacy and usability in the federal statistical system
por: Hotz, V. Joseph, et al.
Publicado: (2022)

A Survey on Differential Privacy for Medical Data Analysis
por: Liu, WeiKang, et al.
Publicado: (2023)

Retracted: Approaches to Federated Computing for the Protection of Patient Privacy and Security Using Medical Applications
por: and Biomechanics, Applied Bionics
Publicado: (2023)

mHealth Systems Need a Privacy-by-Design Approach: Commentary on “Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review”
por: Tewari, Ambuj
Publicado: (2023)

Privacy-preserving continual learning methods for medical image classification: a comparative analysis
por: Verma, Tanvi, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_9nfmm7ggjn2k0t8seuaacrns7q